Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	34,071
2	Florida	30,915
3	Mississippi	30,198
4	Arizona	28,647
5	Alabama	28,298
6	Georgia	26,156
7	South Carolina	25,611
8	Tennessee	24,792
9	Nevada	23,910
10	Texas	23,794
11	Iowa	23,668
12	Arkansas	23,268
13	New York	23,084
14	New Jersey	22,339
15	Rhode Island	21,621
16	Illinois	20,860
17	District of Columbia	20,675
18	North Dakota	20,444
19	Idaho	19,972
20	Nebraska	19,741
21	Delaware	19,356
22	California	19,320
23	Maryland	19,286
24	South Dakota	18,807
25	Massachusetts	18,110
26	Utah	18,057
27	North Carolina	17,670
28	Oklahoma	17,527
29	Missouri	17,017
30	Kansas	16,958
31	Wisconsin	16,268
32	Indiana	16,045
33	Virginia	15,677
34	Connecticut	15,237
35	Minnesota	14,958
36	Kentucky	13,565
37	New Mexico	12,762
38	Michigan	12,323
39	Ohio	11,754
40	Puerto Rico	11,704
41	Pennsylvania	11,610
42	Washington	10,923
43	Colorado	10,703
44	Alaska	9,754
45	Montana	8,455
46	Hawaii	7,633
47	Wyoming	7,509
48	West Virginia	7,085
49	Oregon	6,955
50	New Hampshire	5,660
51	Maine	3,617
52	Vermont	2,698

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	496
2	South Carolina	341
3	South Dakota	302
4	Missouri	270
5	Arkansas	266
6	Wisconsin	257
7	Iowa	224
8	Oklahoma	223
9	Alabama	216
10	Indiana	177
11	Utah	177
12	Mississippi	173
13	Tennessee	165
14	Georgia	160
15	Kentucky	160
16	Illinois	151
17	Louisiana	145
18	Florida	143
19	Nebraska	140
20	Texas	138
21	Delaware	131
22	Virginia	128
23	Hawaii	127
24	North Carolina	126
25	West Virginia	126
26	Alaska	125
27	Idaho	124
28	Minnesota	121
29	Puerto Rico	114
30	Maryland	111
31	Nevada	111
32	Kansas	110
33	Montana	106
34	Ohio	94
35	California	87
36	District of Columbia	85
37	Wyoming	84
38	Michigan	72
39	Arizona	69
40	Colorado	61
41	Massachusetts	61
42	Oregon	53
43	New Mexico	52
44	Pennsylvania	49
45	Washington	46
46	New Jersey	45
47	New York	41
48	Rhode Island	38
49	New Hampshire	30
50	Maine	25
51	Connecticut	21
52	Vermont	12

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,804
2	New York	1,677
3	Massachusetts	1,336
4	Connecticut	1,256
5	Louisiana	1,126
6	Rhode Island	1,010
7	Mississippi	906
8	District of Columbia	872
9	Arizona	731
10	Michigan	692
11	Illinois	675
12	Maryland	634
13	Delaware	631
14	Pennsylvania	618
15	South Carolina	595
16	Florida	586
17	Georgia	583
18	Indiana	510
19	Texas	498
20	Alabama	479
21	Nevada	472
22	New Mexico	392
23	Iowa	386
24	Ohio	377
25	California	364
26	Minnesota	349
27	Colorado	346
28	Arkansas	325
29	New Hampshire	320
30	Virginia	319
31	Tennessee	300
32	Missouri	293
33	North Carolina	293
34	Washington	273
35	Kentucky	246
36	Idaho	232
37	Nebraska	232
38	Oklahoma	228
39	North Dakota	224
40	Wisconsin	209
41	South Dakota	207
42	Kansas	183
43	Puerto Rico	168
44	West Virginia	148
45	Utah	135
46	Montana	127
47	Oregon	120
48	Maine	101
49	Vermont	92
50	Wyoming	72
51	Hawaii	69
52	Alaska	56

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Louisiana	5
2	South Carolina	5
3	Texas	5
4	Arkansas	4
5	Florida	4
6	Georgia	4
7	Kansas	4
8	Mississippi	4
9	Montana	4
10	Tennessee	4
11	Alabama	3
12	North Dakota	3
13	Arizona	2
14	California	2
15	Illinois	2
16	Kentucky	2
17	Massachusetts	2
18	Minnesota	2
19	Nevada	2
20	North Carolina	2
21	Oklahoma	2
22	Puerto Rico	2
23	South Dakota	2
24	Hawaii	1
25	Idaho	1
26	Indiana	1
27	Iowa	1
28	Missouri	1
29	Nebraska	1
30	New Mexico	1
31	Ohio	1
32	Rhode Island	1
33	West Virginia	1
34	Alaska	0
35	Colorado	0
36	Connecticut	0
37	Delaware	0
38	District of Columbia	0
39	Maine	0
40	Maryland	0
41	Michigan	0
42	New Hampshire	0
43	New Jersey	0
44	New York	0
45	Oregon	0
46	Pennsylvania	0
47	Utah	0
48	Vermont	0
49	Virginia	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	145,228	1	99
Trousdale	Tennessee	145,161	2	99
Lafayette	Florida	144,740	3	99
Lincoln	Arkansas	136,671	4	99
Lake	Tennessee	122,149	5	99
Davidson	Tennessee	39,169	173	94
Richland	South Carolina	32,803	275	91
York	South Carolina	17,112	1051	66
Orange	California	16,412	1118	64
Pierce	Washington	8,975	1998	36

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,848	1	99
Galax city	Virginia	4,412	2	99
Randolph	Georgia	3,983	3	99
Terrell	Georgia	3,634	4	99
Emporia city	Virginia	3,554	5	99
Richland	South Carolina	522	688	78
Davidson	Tennessee	402	939	70
Orange	California	344	1069	65
Pierce	Washington	222	1465	53
York	South Carolina	214	1496	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.